Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study
Open accessAbstract
Summary Background Since Dec 31, 2019, the Chinese city of Wuhan has reported an outbreak of atypical pneumonia caused by the 2019 novel coronavirus (2019-nCoV). Cases have been exported to other Chinese cities, as well as internationally, threatening to trigger a global outbreak. Here, we provide an estimate of the size of the epidemic in Wuhan on the basis of the number of cases exported from Wuhan to cities outside mainland China and forecast the extent of the domestic and global public health risks of epidemics, accounting for social and non-pharmaceutical prevention interventions. Methods We used data from Dec 31, 2019, to Jan 28, 2020, on the number of cases exported from Wuhan internationally (known days of symptom onset from Dec 25, 2019, to Jan 19, 2020) to infer the number of infections in Wuhan from Dec 1, 2019, to Jan 25, 2020. Cases exported domestically were then estimated. We forecasted the national and global spread of 2019-nCoV, accounting for the effect of the metropolitan-wide quarantine of Wuhan and surrounding cities, which began Jan 23–24, 2020. We used data on monthly flight bookings from the Official Aviation Guide and data on human mobility across more than 300 prefecture-level cities in mainland China from the Tencent database. Data on confirmed cases were obtained from the reports published by the Chinese Center for Disease Control and Prevention. Serial interval estimates were based on previous studies of severe acute respiratory syndrome coronavirus (SARS-CoV). A susceptible-exposed-infectious-recovered metapopulation model was used to simulate the epidemics across all major cities in China. The basic reproductive number was estimated using Markov Chain Monte Carlo methods and presented using the resulting posterior mean and 95% credibile interval (CrI). Findings In our baseline scenario, we estimated that the basic reproductive number for 2019-nCoV was 2·68 (95% CrI 2·47–2·86) and that 75 815 individuals (95% CrI 37 304–130 330) have been infected in Wuhan as of Jan 25, 2020. The epidemic doubling time was 6·4 days (95% CrI 5·8–7·1). We estimated that in the baseline scenario, Chongqing, Beijing, Shanghai, Guangzhou, and Shenzhen had imported 461 (95% CrI 227–805), 113 (57–193), 98 (49–168), 111 (56–191), and 80 (40–139) infections from Wuhan, respectively. If the transmissibility of 2019-nCoV were similar everywhere domestically and over time, we inferred that epidemics are already growing exponentially in multiple major cities of China with a lag time behind the Wuhan outbreak of about 1–2 weeks. Interpretation Given that 2019-nCoV is no longer contained within Wuhan, other major Chinese cities are probably sustaining localised outbreaks. Large cities overseas with close transport links to China could also become outbreak epicentres, unless substantial public health interventions at both the population and personal levels are implemented immediately. Independent self-sustaining outbreaks in major cities globally could become inevitable because of substantial exportation of presymptomatic cases and in the absence of large-scale public health interventions. Preparedness plans and mitigation interventions should be readied for quick deployment globally. Funding Health and Medical Research Fund (Hong Kong, China).
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Some countries successfully integrated multimodal data from migration maps, mobile phone utilization and health delivery data to forecast the spread of the outbreak and identify potential cases.
“with some countries successfully integrating multimodal data from migration maps, mobile phone utilization and health delivery data to forecast the spread of the outbreak and identify potential cases 58,59 .”
From Multimodal biomedical AI (2022) · cites this paper as [58]
The Data availability and Added value of this study sections say the researchers combined epidemiological reports, flight-booking data, and Tencent intercity mobility data to forecast spread and estimate exported case numbers. The paper does not establish that countries integrated migration maps, mobile-phone utilization, and health-delivery data, or that the model identified individual potential cases. Page numbers are not available in our copy. (LLM, conf=0.85)
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- Authors
- Wu, J. T., Leung, K., & Leung, G. M.
- Affiliations
- University of Hong Kong (HK)
- Corresponding author
- Wu, Joseph T
- Venue
- The Lancet
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 395
- Issue
- 10225
- Pages
- 689-697
- ISSN
- 0099-5355 (ISSN-L)
- 0140-6736
- PMC
- PMC7159271
- OpenAlex
- W3003573988
- Semantic Scholar
- 91badb41dabff195b995823350c9931a9d7fbd24
- Cited by
- 4,389
- References
- 48
- Citations by year
- 2026: 47 · 2025: 94 · 2024: 185 · 2023: 308 · 2022: 621 · 2021: 1119 · 2020: 2007 · 2019: 4 · 2018: 1 · 2017: 1
- FWCI
- 161.29
- Type
- journal article
- Language
- en
- Update notices
- erratum (2020-02-29)
- Open access
- bronze
- doi.org
- Licence
- Locations
- The Lancet (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Government of the Hong Kong Special Administrative Region
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Health Sciences › Medicine › Infectious Diseases › Viral Infections and Outbreaks Research
- Keywords
- SARS-CoV-2, COVID-19 pandemic, epidemic forecasting, basic reproduction number, serial interval, human mobility, international travel, quarantine, Wuhan, China, epidemic size estimation, epidemic doubling time, non-pharmaceutical interventions, SARS-CoV, atypical pneumonia, human-to-human transmission
- MeSH terms
- China, Computer Simulation, Forecasting, Hospitalization, Humans, Markov Chains, Monte Carlo Method, Prevalence, Coronavirus Infections, Internationality, Infectious Disease Incubation Period, Epidemics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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